Overview

Dataset statistics

Number of variables19
Number of observations1368
Missing cells7477
Missing cells (%)28.8%
Duplicate rows33
Duplicate rows (%)2.4%
Total size in memory203.2 KiB
Average record size in memory152.1 B

Variable types

Numeric10
Categorical7
Text2

Alerts

palette has constant value ""Constant
origin_product has constant value ""Constant
volume has constant value ""Constant
Dataset has 33 (2.4%) duplicate rowsDuplicates
L1 has 562 (41.1%) missing valuesMissing
M1 has 993 (72.6%) missing valuesMissing
N2 has 997 (72.9%) missing valuesMissing
O3 has 510 (37.3%) missing valuesMissing
P8 has 1108 (81.0%) missing valuesMissing
D9 has 1302 (95.2%) missing valuesMissing
H0 has 1125 (82.2%) missing valuesMissing
Z5 has 880 (64.3%) missing valuesMissing

Reproduction

Analysis started2024-04-10 02:43:33.178803
Analysis finished2024-04-10 02:44:01.263898
Duration28.09 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

product
Real number (ℝ)

Distinct1297
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9877992.4
Minimum815975
Maximum12929380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2024-04-09T23:44:01.482827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum815975
5-th percentile816327.05
Q11954911
median12927988
Q312928636
95-th percentile12929207
Maximum12929380
Range12113405
Interquartile range (IQR)10973725

Descriptive statistics

Standard deviation5253384
Coefficient of variation (CV)0.5318271
Kurtosis-0.69373886
Mean9877992.4
Median Absolute Deviation (MAD)673
Skewness-1.1429844
Sum1.3513094 × 1010
Variance2.7598044 × 1013
MonotonicityNot monotonic
2024-04-09T23:44:01.899693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12928426 2
 
0.1%
816940 2
 
0.1%
12928381 2
 
0.1%
12928612 2
 
0.1%
12929193 2
 
0.1%
817505 2
 
0.1%
817956 2
 
0.1%
12927813 2
 
0.1%
12929195 2
 
0.1%
12928010 2
 
0.1%
Other values (1287) 1348
98.5%
ValueCountFrequency (%)
815975 1
0.1%
815977 2
0.1%
815983 1
0.1%
815984 1
0.1%
815991 1
0.1%
815992 1
0.1%
815997 1
0.1%
815999 1
0.1%
816001 1
0.1%
816003 1
0.1%
ValueCountFrequency (%)
12929380 1
0.1%
12929379 1
0.1%
12929377 1
0.1%
12929376 1
0.1%
12929372 2
0.1%
12929370 1
0.1%
12929366 1
0.1%
12929362 1
0.1%
12929361 1
0.1%
12929359 1
0.1%

palette
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
7
1368 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1368
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row7
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
7 1368
100.0%

Length

2024-04-09T23:44:02.290569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-09T23:44:02.614466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
7 1368
100.0%

Most occurring characters

ValueCountFrequency (%)
7 1368
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 1368
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 1368
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 1368
100.0%

origin_product
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
Tinta Suvinil Fosco Completo
1368 

Length

Max length28
Median length28
Mean length28
Min length28

Characters and Unicode

Total characters38304
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTinta Suvinil Fosco Completo
2nd rowTinta Suvinil Fosco Completo
3rd rowTinta Suvinil Fosco Completo
4th rowTinta Suvinil Fosco Completo
5th rowTinta Suvinil Fosco Completo

Common Values

ValueCountFrequency (%)
Tinta Suvinil Fosco Completo 1368
100.0%

Length

2024-04-09T23:44:02.887377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-09T23:44:03.143295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
tinta 1368
25.0%
suvinil 1368
25.0%
fosco 1368
25.0%
completo 1368
25.0%

Most occurring characters

ValueCountFrequency (%)
o 5472
14.3%
4104
 
10.7%
i 4104
 
10.7%
l 2736
 
7.1%
n 2736
 
7.1%
t 2736
 
7.1%
p 1368
 
3.6%
m 1368
 
3.6%
C 1368
 
3.6%
c 1368
 
3.6%
Other values (8) 10944
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5472
14.3%
4104
 
10.7%
i 4104
 
10.7%
l 2736
 
7.1%
n 2736
 
7.1%
t 2736
 
7.1%
p 1368
 
3.6%
m 1368
 
3.6%
C 1368
 
3.6%
c 1368
 
3.6%
Other values (8) 10944
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5472
14.3%
4104
 
10.7%
i 4104
 
10.7%
l 2736
 
7.1%
n 2736
 
7.1%
t 2736
 
7.1%
p 1368
 
3.6%
m 1368
 
3.6%
C 1368
 
3.6%
c 1368
 
3.6%
Other values (8) 10944
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5472
14.3%
4104
 
10.7%
i 4104
 
10.7%
l 2736
 
7.1%
n 2736
 
7.1%
t 2736
 
7.1%
p 1368
 
3.6%
m 1368
 
3.6%
C 1368
 
3.6%
c 1368
 
3.6%
Other values (8) 10944
28.6%

uf_r
Categorical

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
1 - N/NE
162 
9 - S
162 
10 - S
142 
2 - N/NE
142 
7 - SE
142 
Other values (5)
618 

Length

Max length8
Median length6
Mean length6.5190058
Min length5

Characters and Unicode

Total characters8918
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 - N/NE
2nd row1 - N/NE
3rd row1 - N/NE
4th row1 - N/NE
5th row1 - N/NE

Common Values

ValueCountFrequency (%)
1 - N/NE 162
11.8%
9 - S 162
11.8%
10 - S 142
10.4%
2 - N/NE 142
10.4%
7 - SE 142
10.4%
3 - N/NE 132
9.6%
5 - CO 124
9.1%
6 - SE 124
9.1%
8 - SE 122
8.9%
4 - CO 116
8.5%

Length

2024-04-09T23:44:03.470189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-09T23:44:03.844069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1368
33.3%
n/ne 436
 
10.6%
se 388
 
9.5%
s 304
 
7.4%
co 240
 
5.8%
1 162
 
3.9%
9 162
 
3.9%
10 142
 
3.5%
2 142
 
3.5%
7 142
 
3.5%
Other values (5) 618
15.1%

Most occurring characters

ValueCountFrequency (%)
2736
30.7%
- 1368
15.3%
N 872
 
9.8%
E 824
 
9.2%
S 692
 
7.8%
/ 436
 
4.9%
1 304
 
3.4%
C 240
 
2.7%
O 240
 
2.7%
9 162
 
1.8%
Other values (8) 1044
 
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2736
30.7%
- 1368
15.3%
N 872
 
9.8%
E 824
 
9.2%
S 692
 
7.8%
/ 436
 
4.9%
1 304
 
3.4%
C 240
 
2.7%
O 240
 
2.7%
9 162
 
1.8%
Other values (8) 1044
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2736
30.7%
- 1368
15.3%
N 872
 
9.8%
E 824
 
9.2%
S 692
 
7.8%
/ 436
 
4.9%
1 304
 
3.4%
C 240
 
2.7%
O 240
 
2.7%
9 162
 
1.8%
Other values (8) 1044
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2736
30.7%
- 1368
15.3%
N 872
 
9.8%
E 824
 
9.2%
S 692
 
7.8%
/ 436
 
4.9%
1 304
 
3.4%
C 240
 
2.7%
O 240
 
2.7%
9 162
 
1.8%
Other values (8) 1044
 
11.7%

type_fs_base
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
K
924 
Q
223 
J
221 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1368
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJ
2nd rowJ
3rd rowJ
4th rowJ
5th rowJ

Common Values

ValueCountFrequency (%)
K 924
67.5%
Q 223
 
16.3%
J 221
 
16.2%

Length

2024-04-09T23:44:04.267933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-09T23:44:04.553843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
k 924
67.5%
q 223
 
16.3%
j 221
 
16.2%

Most occurring characters

ValueCountFrequency (%)
K 924
67.5%
Q 223
 
16.3%
J 221
 
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
K 924
67.5%
Q 223
 
16.3%
J 221
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
K 924
67.5%
Q 223
 
16.3%
J 221
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
K 924
67.5%
Q 223
 
16.3%
J 221
 
16.2%

volume
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
16
1368 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2736
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16
2nd row16
3rd row16
4th row16
5th row16

Common Values

ValueCountFrequency (%)
16 1368
100.0%

Length

2024-04-09T23:44:04.897732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-09T23:44:05.171643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
16 1368
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1368
50.0%
6 1368
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1368
50.0%
6 1368
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1368
50.0%
6 1368
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1368
50.0%
6 1368
50.0%
Distinct1226
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
2024-04-09T23:44:05.706221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9656433
Min length5

Characters and Unicode

Total characters8161
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1094 ?
Unique (%)80.0%

Sample

1st row1Y- 25
2nd row1Y-R92
3rd row0R-I50
4th row0Y-O08
5th row0Y-A93
ValueCountFrequency (%)
0y 30
 
2.0%
0b 18
 
1.2%
0g 17
 
1.1%
0r 15
 
1.0%
3y 9
 
0.6%
5y 7
 
0.5%
9b 6
 
0.4%
86 6
 
0.4%
09 5
 
0.3%
84 5
 
0.3%
Other values (1186) 1402
92.2%
2024-04-09T23:44:06.645919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1369
16.8%
0 1112
13.6%
Y 600
 
7.4%
2 369
 
4.5%
4 363
 
4.4%
R 363
 
4.4%
1 344
 
4.2%
5 341
 
4.2%
3 336
 
4.1%
6 328
 
4.0%
Other values (37) 2636
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8161
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 1369
16.8%
0 1112
13.6%
Y 600
 
7.4%
2 369
 
4.5%
4 363
 
4.4%
R 363
 
4.4%
1 344
 
4.2%
5 341
 
4.2%
3 336
 
4.1%
6 328
 
4.0%
Other values (37) 2636
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8161
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 1369
16.8%
0 1112
13.6%
Y 600
 
7.4%
2 369
 
4.5%
4 363
 
4.4%
R 363
 
4.4%
1 344
 
4.2%
5 341
 
4.2%
3 336
 
4.1%
6 328
 
4.0%
Other values (37) 2636
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8161
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 1369
16.8%
0 1112
13.6%
Y 600
 
7.4%
2 369
 
4.5%
4 363
 
4.4%
R 363
 
4.4%
1 344
 
4.2%
5 341
 
4.2%
3 336
 
4.1%
6 328
 
4.0%
Other values (37) 2636
32.3%

principal_group
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
Y
600 
B
292 
R
247 
G
223 
N
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1368
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowR
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 600
43.9%
B 292
21.3%
R 247
18.1%
G 223
 
16.3%
N 6
 
0.4%

Length

2024-04-09T23:44:07.024798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-09T23:44:07.322701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
y 600
43.9%
b 292
21.3%
r 247
18.1%
g 223
 
16.3%
n 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
Y 600
43.9%
B 292
21.3%
R 247
18.1%
G 223
 
16.3%
N 6
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 600
43.9%
B 292
21.3%
R 247
18.1%
G 223
 
16.3%
N 6
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 600
43.9%
B 292
21.3%
R 247
18.1%
G 223
 
16.3%
N 6
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 600
43.9%
B 292
21.3%
R 247
18.1%
G 223
 
16.3%
N 6
 
0.4%

subgroup
Categorical

Distinct42
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
0Y
325 
0B
164 
0G
155 
0R
145 
5Y
55 
Other values (37)
524 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2736
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Y
2nd row1Y
3rd row0R
4th row0Y
5th row0Y

Common Values

ValueCountFrequency (%)
0Y 325
23.8%
0B 164
12.0%
0G 155
11.3%
0R 145
 
10.6%
5Y 55
 
4.0%
3Y 37
 
2.7%
1Y 34
 
2.5%
4Y 32
 
2.3%
8Y 27
 
2.0%
9Y 26
 
1.9%
Other values (32) 368
26.9%

Length

2024-04-09T23:44:07.650597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0y 325
23.8%
0b 164
12.0%
0g 155
11.3%
0r 145
 
10.6%
5y 55
 
4.0%
3y 37
 
2.7%
1y 34
 
2.5%
4y 32
 
2.3%
8y 27
 
2.0%
9y 26
 
1.9%
Other values (32) 368
26.9%

Most occurring characters

ValueCountFrequency (%)
0 795
29.1%
Y 600
21.9%
B 292
 
10.7%
R 247
 
9.0%
G 223
 
8.2%
5 84
 
3.1%
3 76
 
2.8%
4 73
 
2.7%
1 60
 
2.2%
9 59
 
2.2%
Other values (6) 227
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 795
29.1%
Y 600
21.9%
B 292
 
10.7%
R 247
 
9.0%
G 223
 
8.2%
5 84
 
3.1%
3 76
 
2.8%
4 73
 
2.7%
1 60
 
2.2%
9 59
 
2.2%
Other values (6) 227
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 795
29.1%
Y 600
21.9%
B 292
 
10.7%
R 247
 
9.0%
G 223
 
8.2%
5 84
 
3.1%
3 76
 
2.8%
4 73
 
2.7%
1 60
 
2.2%
9 59
 
2.2%
Other values (6) 227
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 795
29.1%
Y 600
21.9%
B 292
 
10.7%
R 247
 
9.0%
G 223
 
8.2%
5 84
 
3.1%
3 76
 
2.8%
4 73
 
2.7%
1 60
 
2.2%
9 59
 
2.2%
Other values (6) 227
 
8.3%

L1
Real number (ℝ)

MISSING 

Distinct29
Distinct (%)3.6%
Missing562
Missing (%)41.1%
Infinite0
Infinite (%)0.0%
Mean48.430521
Minimum5
Maximum650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2024-04-09T23:44:07.983491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q110
median25
Q350
95-th percentile155
Maximum650
Range645
Interquartile range (IQR)40

Descriptive statistics

Standard deviation82.202897
Coefficient of variation (CV)1.6973366
Kurtosis31.44996
Mean48.430521
Median Absolute Deviation (MAD)15
Skewness5.0249007
Sum39035
Variance6757.3163
MonotonicityNot monotonic
2024-04-09T23:44:08.321381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
15 131
 
9.6%
10 109
 
8.0%
5 97
 
7.1%
25 70
 
5.1%
20 65
 
4.8%
35 51
 
3.7%
40 34
 
2.5%
30 26
 
1.9%
55 21
 
1.5%
60 19
 
1.4%
Other values (19) 183
 
13.4%
(Missing) 562
41.1%
ValueCountFrequency (%)
5 97
7.1%
10 109
8.0%
15 131
9.6%
20 65
4.8%
25 70
5.1%
30 26
 
1.9%
35 51
 
3.7%
40 34
 
2.5%
45 18
 
1.3%
50 15
 
1.1%
ValueCountFrequency (%)
650 3
 
0.2%
640 1
 
0.1%
635 6
 
0.4%
230 17
1.2%
215 13
1.0%
155 6
 
0.4%
135 4
 
0.3%
130 9
0.7%
120 14
1.0%
115 13
1.0%

M1
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)4.5%
Missing993
Missing (%)72.6%
Infinite0
Infinite (%)0.0%
Mean39.266667
Minimum5
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2024-04-09T23:44:08.649276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q110
median15
Q335
95-th percentile165.5
Maximum330
Range325
Interquartile range (IQR)25

Descriptive statistics

Standard deviation65.036463
Coefficient of variation (CV)1.6562766
Kurtosis10.860421
Mean39.266667
Median Absolute Deviation (MAD)10
Skewness3.2707852
Sum14725
Variance4229.7415
MonotonicityNot monotonic
2024-04-09T23:44:08.970174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
10 100
 
7.3%
5 58
 
4.2%
20 43
 
3.1%
15 43
 
3.1%
30 20
 
1.5%
45 18
 
1.3%
60 16
 
1.2%
35 15
 
1.1%
25 9
 
0.7%
130 7
 
0.5%
Other values (7) 46
 
3.4%
(Missing) 993
72.6%
ValueCountFrequency (%)
5 58
4.2%
10 100
7.3%
15 43
3.1%
20 43
3.1%
25 9
 
0.7%
30 20
 
1.5%
35 15
 
1.1%
45 18
 
1.3%
50 6
 
0.4%
60 16
 
1.2%
ValueCountFrequency (%)
330 6
 
0.4%
320 7
 
0.5%
190 6
 
0.4%
155 7
 
0.5%
130 7
 
0.5%
95 7
 
0.5%
70 7
 
0.5%
60 16
1.2%
50 6
 
0.4%
45 18
1.3%

N2
Real number (ℝ)

MISSING 

Distinct18
Distinct (%)4.9%
Missing997
Missing (%)72.9%
Infinite0
Infinite (%)0.0%
Mean51.347709
Minimum5
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2024-04-09T23:44:09.281073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q115
median30
Q340
95-th percentile305
Maximum365
Range360
Interquartile range (IQR)25

Descriptive statistics

Standard deviation81.751538
Coefficient of variation (CV)1.5921166
Kurtosis7.9568371
Mean51.347709
Median Absolute Deviation (MAD)10
Skewness3.0118754
Sum19050
Variance6683.3139
MonotonicityNot monotonic
2024-04-09T23:44:09.638960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
30 69
 
5.0%
20 51
 
3.7%
10 45
 
3.3%
25 34
 
2.5%
15 28
 
2.0%
40 26
 
1.9%
5 25
 
1.8%
35 20
 
1.5%
75 13
 
1.0%
365 9
 
0.7%
Other values (8) 51
 
3.7%
(Missing) 997
72.9%
ValueCountFrequency (%)
5 25
 
1.8%
10 45
3.3%
15 28
2.0%
20 51
3.7%
25 34
2.5%
30 69
5.0%
35 20
 
1.5%
40 26
 
1.9%
50 7
 
0.5%
55 8
 
0.6%
ValueCountFrequency (%)
365 9
0.7%
350 7
0.5%
305 7
0.5%
230 6
0.4%
180 1
 
0.1%
95 8
0.6%
75 13
1.0%
60 7
0.5%
55 8
0.6%
50 7
0.5%

O3
Real number (ℝ)

MISSING 

Distinct26
Distinct (%)3.0%
Missing510
Missing (%)37.3%
Infinite0
Infinite (%)0.0%
Mean40.844988
Minimum5
Maximum1310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2024-04-09T23:44:09.991845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q110
median20
Q350
95-th percentile130
Maximum1310
Range1305
Interquartile range (IQR)40

Descriptive statistics

Standard deviation75.66556
Coefficient of variation (CV)1.8525054
Kurtosis101.42683
Mean40.844988
Median Absolute Deviation (MAD)15
Skewness7.8227529
Sum35045
Variance5725.277
MonotonicityNot monotonic
2024-04-09T23:44:10.357728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
5 212
15.5%
10 121
 
8.8%
15 79
 
5.8%
25 63
 
4.6%
20 52
 
3.8%
35 51
 
3.7%
55 41
 
3.0%
30 36
 
2.6%
75 29
 
2.1%
60 24
 
1.8%
Other values (16) 150
 
11.0%
(Missing) 510
37.3%
ValueCountFrequency (%)
5 212
15.5%
10 121
8.8%
15 79
 
5.8%
20 52
 
3.8%
25 63
 
4.6%
30 36
 
2.6%
35 51
 
3.7%
40 18
 
1.3%
50 13
 
1.0%
55 41
 
3.0%
ValueCountFrequency (%)
1310 1
 
0.1%
475 7
 
0.5%
325 3
 
0.2%
300 7
 
0.5%
265 6
 
0.4%
150 7
 
0.5%
135 8
0.6%
130 19
1.4%
120 7
 
0.5%
100 7
 
0.5%

P8
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)3.8%
Missing1108
Missing (%)81.0%
Infinite0
Infinite (%)0.0%
Mean22.173077
Minimum5
Maximum195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2024-04-09T23:44:10.871562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q15
median10
Q320
95-th percentile100
Maximum195
Range190
Interquartile range (IQR)15

Descriptive statistics

Standard deviation33.996775
Coefficient of variation (CV)1.5332457
Kurtosis16.236493
Mean22.173077
Median Absolute Deviation (MAD)5
Skewness3.8697554
Sum5765
Variance1155.7807
MonotonicityNot monotonic
2024-04-09T23:44:11.185462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 89
 
6.5%
10 56
 
4.1%
15 29
 
2.1%
20 24
 
1.8%
35 19
 
1.4%
40 14
 
1.0%
30 8
 
0.6%
45 7
 
0.5%
100 7
 
0.5%
195 7
 
0.5%
(Missing) 1108
81.0%
ValueCountFrequency (%)
5 89
6.5%
10 56
4.1%
15 29
 
2.1%
20 24
 
1.8%
30 8
 
0.6%
35 19
 
1.4%
40 14
 
1.0%
45 7
 
0.5%
100 7
 
0.5%
195 7
 
0.5%
ValueCountFrequency (%)
195 7
 
0.5%
100 7
 
0.5%
45 7
 
0.5%
40 14
 
1.0%
35 19
 
1.4%
30 8
 
0.6%
20 24
 
1.8%
15 29
 
2.1%
10 56
4.1%
5 89
6.5%

D9
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)12.1%
Missing1302
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean40.378788
Minimum5
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2024-04-09T23:44:11.473369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q15
median20
Q351.25
95-th percentile150
Maximum150
Range145
Interquartile range (IQR)46.25

Descriptive statistics

Standard deviation47.936742
Coefficient of variation (CV)1.1871764
Kurtosis1.1896856
Mean40.378788
Median Absolute Deviation (MAD)15
Skewness1.5828808
Sum2665
Variance2297.9312
MonotonicityNot monotonic
2024-04-09T23:44:11.797266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 22
 
1.6%
150 9
 
0.7%
20 8
 
0.6%
55 7
 
0.5%
15 7
 
0.5%
40 6
 
0.4%
35 6
 
0.4%
105 1
 
0.1%
(Missing) 1302
95.2%
ValueCountFrequency (%)
5 22
1.6%
15 7
 
0.5%
20 8
 
0.6%
35 6
 
0.4%
40 6
 
0.4%
55 7
 
0.5%
105 1
 
0.1%
150 9
0.7%
ValueCountFrequency (%)
150 9
0.7%
105 1
 
0.1%
55 7
 
0.5%
40 6
 
0.4%
35 6
 
0.4%
20 8
 
0.6%
15 7
 
0.5%
5 22
1.6%

H0
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)5.3%
Missing1125
Missing (%)82.2%
Infinite0
Infinite (%)0.0%
Mean48.024691
Minimum5
Maximum570
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2024-04-09T23:44:12.097169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q115
median25
Q337.5
95-th percentile200
Maximum570
Range565
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation92.669364
Coefficient of variation (CV)1.9296191
Kurtosis22.901931
Mean48.024691
Median Absolute Deviation (MAD)10
Skewness4.6506412
Sum11670
Variance8587.611
MonotonicityNot monotonic
2024-04-09T23:44:12.383077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
15 52
 
3.8%
25 29
 
2.1%
5 27
 
2.0%
35 25
 
1.8%
20 24
 
1.8%
45 19
 
1.4%
30 17
 
1.2%
55 14
 
1.0%
200 11
 
0.8%
10 8
 
0.6%
Other values (3) 17
 
1.2%
(Missing) 1125
82.2%
ValueCountFrequency (%)
5 27
2.0%
10 8
 
0.6%
15 52
3.8%
20 24
1.8%
25 29
2.1%
30 17
 
1.2%
35 25
1.8%
40 6
 
0.4%
45 19
 
1.4%
55 14
 
1.0%
ValueCountFrequency (%)
570 6
 
0.4%
200 11
 
0.8%
120 5
 
0.4%
55 14
1.0%
45 19
1.4%
40 6
 
0.4%
35 25
1.8%
30 17
1.2%
25 29
2.1%
20 24
1.8%

Z5
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)3.5%
Missing880
Missing (%)64.3%
Infinite0
Infinite (%)0.0%
Mean30.030738
Minimum5
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2024-04-09T23:44:12.682981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q110
median15
Q325
95-th percentile145
Maximum460
Range455
Interquartile range (IQR)15

Descriptive statistics

Standard deviation45.635475
Coefficient of variation (CV)1.5196255
Kurtosis21.547714
Mean30.030738
Median Absolute Deviation (MAD)5
Skewness3.9312753
Sum14655
Variance2082.5966
MonotonicityNot monotonic
2024-04-09T23:44:12.983884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
10 108
 
7.9%
5 88
 
6.4%
15 85
 
6.2%
20 57
 
4.2%
25 30
 
2.2%
35 21
 
1.5%
40 19
 
1.4%
80 17
 
1.2%
30 16
 
1.2%
155 8
 
0.6%
Other values (7) 39
 
2.9%
(Missing) 880
64.3%
ValueCountFrequency (%)
5 88
6.4%
10 108
7.9%
15 85
6.2%
20 57
4.2%
25 30
 
2.2%
30 16
 
1.2%
35 21
 
1.5%
40 19
 
1.4%
45 7
 
0.5%
75 4
 
0.3%
ValueCountFrequency (%)
460 1
 
0.1%
230 6
 
0.4%
185 7
 
0.5%
155 8
0.6%
145 7
 
0.5%
90 7
 
0.5%
80 17
1.2%
75 4
 
0.3%
45 7
 
0.5%
40 19
1.4%
Distinct550
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
2024-04-09T23:44:13.395751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.130117
Min length12

Characters and Unicode

Total characters17962
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique320 ?
Unique (%)23.4%

Sample

1st row R$ 615,00
2nd row R$ 738,00
3rd row R$ 633,00
4th row R$ 696,00
5th row R$ 898,00
ValueCountFrequency (%)
r 1368
50.0%
833,00 48
 
1.8%
738,00 28
 
1.0%
615,00 20
 
0.7%
910,00 19
 
0.7%
923,00 19
 
0.7%
633,00 18
 
0.7%
865,00 17
 
0.6%
726,00 16
 
0.6%
841,50 16
 
0.6%
Other values (541) 1167
42.7%
2024-04-09T23:44:14.266473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5472
30.5%
0 2816
15.7%
R 1368
 
7.6%
$ 1368
 
7.6%
, 1368
 
7.6%
1 1096
 
6.1%
. 773
 
4.3%
5 668
 
3.7%
8 574
 
3.2%
3 485
 
2.7%
Other values (5) 1974
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5472
30.5%
0 2816
15.7%
R 1368
 
7.6%
$ 1368
 
7.6%
, 1368
 
7.6%
1 1096
 
6.1%
. 773
 
4.3%
5 668
 
3.7%
8 574
 
3.2%
3 485
 
2.7%
Other values (5) 1974
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5472
30.5%
0 2816
15.7%
R 1368
 
7.6%
$ 1368
 
7.6%
, 1368
 
7.6%
1 1096
 
6.1%
. 773
 
4.3%
5 668
 
3.7%
8 574
 
3.2%
3 485
 
2.7%
Other values (5) 1974
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5472
30.5%
0 2816
15.7%
R 1368
 
7.6%
$ 1368
 
7.6%
, 1368
 
7.6%
1 1096
 
6.1%
. 773
 
4.3%
5 668
 
3.7%
8 574
 
3.2%
3 485
 
2.7%
Other values (5) 1974
 
11.0%

final_price_n
Real number (ℝ)

Distinct550
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1210.3896
Minimum136
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2024-04-09T23:44:14.693336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum136
5-th percentile591
Q1833
median1120.5
Q31621
95-th percentile1922.65
Maximum1998
Range1862
Interquartile range (IQR)788

Descriptive statistics

Standard deviation459.55682
Coefficient of variation (CV)0.37967677
Kurtosis-1.2811759
Mean1210.3896
Median Absolute Deviation (MAD)394.5
Skewness0.082383332
Sum1655813
Variance211192.47
MonotonicityNot monotonic
2024-04-09T23:44:15.094208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
833 48
 
3.5%
738 28
 
2.0%
615 20
 
1.5%
910 19
 
1.4%
923 19
 
1.4%
633 18
 
1.3%
865 17
 
1.2%
726 16
 
1.2%
841.5 16
 
1.2%
710.5 15
 
1.1%
Other values (540) 1152
84.2%
ValueCountFrequency (%)
136 2
0.1%
188.5 1
0.1%
193.5 1
0.1%
216 1
0.1%
223.5 1
0.1%
272 1
0.1%
276 1
0.1%
293.5 1
0.1%
306 1
0.1%
315.5 1
0.1%
ValueCountFrequency (%)
1998 1
0.1%
1996 2
0.1%
1994 1
0.1%
1993 1
0.1%
1992 1
0.1%
1991 2
0.1%
1988 1
0.1%
1987 1
0.1%
1986 2
0.1%
1984 1
0.1%

Interactions

2024-04-09T23:43:56.781988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:34.215044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:36.863020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:39.305234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:41.758813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:44.442949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:46.996129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:49.379365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:51.699704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:54.171825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:57.058899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:34.489955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:37.136930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:39.564151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:42.008731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:44.712863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:47.250047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:49.639280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:51.951539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:54.411748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:57.337809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:34.777688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:37.412843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:39.799076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:42.230661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:44.974779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:47.450983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:49.867208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:52.167469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:54.637675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:57.592728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:35.048600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:37.645768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:40.074986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:42.480582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:45.224698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:47.655918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:50.078141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:52.424387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:54.902593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:57.866639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:35.301520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:37.898686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:40.310911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:42.755491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:45.483616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:47.871849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:50.303067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:52.651313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:55.158508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:58.166542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:35.573433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:38.149606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:40.568828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:42.994414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:45.759527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:48.110773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:50.523997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:52.911232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:55.364443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:58.458448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:35.837349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:38.351542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:40.801119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:43.210346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:46.007447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:48.398680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:50.730930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:53.156152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:55.647351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:58.710368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:36.087267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:38.557475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:40.994056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:43.643206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:46.252368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:48.601615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:50.984850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:53.398075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:55.846288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:59.014271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:36.318193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:38.802395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:41.272967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:43.901124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:46.501288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:48.833539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:51.224772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:53.627001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:56.295144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:59.255193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:36.552119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:39.018327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:41.505892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:44.155041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:46.706224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:49.097456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:51.436709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:53.887917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-09T23:43:56.520071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-04-09T23:43:59.692052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-09T23:44:00.452810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-09T23:44:00.992985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

productpaletteorigin_productuf_rtype_fs_basevolumecode_collorprincipal_groupsubgroupL1M1N2O3P8D9H0Z5final_pricefinal_price_n
0129293057Tinta Suvinil Fosco Completo1 - N/NEJ161Y- 25Y1YNaNNaNNaN5.005.00NaNNaN10.00R$ 615,00615.00
1129279737Tinta Suvinil Fosco Completo1 - N/NEJ161Y-R92Y1YNaNNaN10.00NaNNaNNaNNaN20.00R$ 738,00738.00
2129288007Tinta Suvinil Fosco Completo1 - N/NEJ160R-I50R0RNaNNaNNaNNaN10.00NaNNaN10.00R$ 633,00633.00
3129279177Tinta Suvinil Fosco Completo1 - N/NEJ160Y-O08Y0YNaNNaNNaN10.00NaNNaNNaN10.00R$ 696,00696.00
48176537Tinta Suvinil Fosco Completo1 - N/NEJ160Y-A93Y0YNaN20.00NaNNaNNaNNaN15.005.00R$ 898,00898.00
5129281927Tinta Suvinil Fosco Completo1 - N/NEJ160G-G00G0GNaN20.00NaNNaNNaNNaN15.005.00R$ 898,00898.00
6129277307Tinta Suvinil Fosco Completo1 - N/NEJ160Y-O32Y0Y25.00NaNNaN5.005.00NaN20.00NaNR$ 1.045,001045.00
7129280437Tinta Suvinil Fosco Completo1 - N/NEJ162Y-A08Y2Y55.0010.00NaNNaNNaNNaNNaNNaNR$ 1.962,001962.00
88179097Tinta Suvinil Fosco Completo1 - N/NEJ160G-D50G0G5.0030.00NaN10.00NaNNaN45.00NaNR$ 1.427,001427.00
98179517Tinta Suvinil Fosco Completo1 - N/NEJ165G-E49G5G15.00NaN5.0070.00NaNNaNNaN25.00R$ 1.483,001483.00
productpaletteorigin_productuf_rtype_fs_basevolumecode_collorprincipal_groupsubgroupL1M1N2O3P8D9H0Z5final_pricefinal_price_n
1358129284817Tinta Suvinil Fosco Completo9 - SK160B-R26B0B20.00NaNNaN35.0030.00NaNNaNNaNR$ 1.698,001698.00
1359129283687Tinta Suvinil Fosco Completo9 - SK165G-N54G5GNaNNaN20.00NaNNaNNaNNaN5.00R$ 748,00748.00
13608168127Tinta Suvinil Fosco Completo9 - SK160B- 86B0B15.00NaN60.00NaNNaNNaNNaNNaNR$ 1.807,001807.00
13618167327Tinta Suvinil Fosco Completo9 - SK160Y-I49Y0Y15.00NaN60.00NaNNaNNaNNaNNaNR$ 1.843,001843.00
13628160017Tinta Suvinil Fosco Completo9 - SK160Y-R71Y0YNaN50.00NaNNaNNaNNaNNaNNaNR$ 1.176,001176.00
1363129285027Tinta Suvinil Fosco Completo9 - SK161B-U28B1B5.00NaNNaNNaN5.00NaN10.00NaNR$ 778,50778.50
13648165427Tinta Suvinil Fosco Completo9 - SK165Y-O87Y5Y60.00NaNNaNNaNNaNNaN40.00NaNR$ 1.954,001954.00
1365129284357Tinta Suvinil Fosco Completo9 - SK162B-U20B2B105.00NaN40.0040.00NaNNaNNaNNaNR$ 1.757,001757.00
1366129286717Tinta Suvinil Fosco Completo9 - SK163B-E71B3B60.00NaNNaNNaNNaNNaN40.00NaNR$ 1.818,001818.00
1367129275587Tinta Suvinil Fosco Completo9 - SK160Y-E23Y0Y60.00NaNNaN25.0010.00NaN200.005.00R$ 1.879,001879.00

Duplicate rows

Most frequently occurring

productpaletteorigin_productuf_rtype_fs_basevolumecode_collorprincipal_groupsubgroupL1M1N2O3P8D9H0Z5final_pricefinal_price_n# duplicates
08161777Tinta Suvinil Fosco Completo10 - SK160Y- 04Y0Y15.00NaNNaN5.00NaNNaNNaN10.00R$ 874,50874.502
18169407Tinta Suvinil Fosco Completo10 - SK160Y-E44Y0YNaNNaN25.0010.00NaNNaNNaNNaNR$ 833,00833.002
2129273447Tinta Suvinil Fosco Completo10 - SK160Y-A80Y0Y15.00NaNNaN15.00NaNNaN15.005.00R$ 956,00956.002
3129273577Tinta Suvinil Fosco Completo10 - SK165Y-U87Y5Y10.0015.00NaNNaNNaNNaNNaNNaNR$ 710,50710.502
4129273747Tinta Suvinil Fosco Completo10 - SK163Y-I81Y3Y20.00NaNNaN20.00NaNNaNNaNNaNR$ 923,00923.002
5129274227Tinta Suvinil Fosco Completo10 - SK160G-H53G0G10.00NaNNaN5.00NaNNaNNaNNaNR$ 608,00608.002
6129274237Tinta Suvinil Fosco Completo10 - SK164G-L28G4G20.00NaNNaNNaNNaNNaNNaNNaNR$ 748,50748.502
7129274387Tinta Suvinil Fosco Completo10 - SK160B- 14B0B5.00NaN15.00NaNNaNNaNNaNNaNR$ 721,00721.002
8129274437Tinta Suvinil Fosco Completo10 - SK160B-R39B0BNaNNaNNaN10.005.00NaNNaNNaNR$ 700,00700.002
9129274547Tinta Suvinil Fosco Completo10 - SK160R-M23R0RNaNNaNNaN5.00NaNNaNNaNNaNR$ 453,00453.002